GitBook: [master] 62 pages modified

woop · gitbook-bot · commit 913e497ef955 · 2021-08-31T16:50:09.000Z
diff --git a/docs/SUMMARY.md b/docs/SUMMARY.md
@@ -29,6 +29,7 @@
 * [Overview](tutorials/tutorials-overview.md)
 * [Driver ranking](tutorials/driver-ranking-with-feast.md)
 * [Fraud detection on GCP](tutorials/fraud-detection.md)
+* [Real-time credit scoring on AWS](tutorials/real-time-credit-scoring-on-aws.md)
 
 ## How-to Guides
 
diff --git a/docs/tutorials/driver-ranking-with-feast.md b/docs/tutorials/driver-ranking-with-feast.md
@@ -1,21 +1,21 @@
 ---
 description: >-
   Making a prediction using a linear regression model is a common use case in
-  ML. This model predicts if a driver will complete a trip based on a features
+  ML. This model predicts if a driver will complete a trip based on features
   ingested into Feast.
 ---
 
 # Driver ranking
 
-In this example you'll learn how to use some of the key functionality in Feast. The tutorial runs in both local mode and on the Google Cloud Platform \(GCP\). For GCP, you must have access to a GCP project already, including read and write permissions to BigQuery.
+In this example, you'll learn how to use some of the key functionality in Feast. The tutorial runs in both local mode and on the Google Cloud Platform \(GCP\). For GCP, you must have access to a GCP project already, including read and write permissions to BigQuery.
 
 ## [Driver Ranking Example](https://github.com/feast-dev/feast-driver-ranking-tutorial)
 
-This tutorial guides you in how to use Feast with [scikit-learn](https://scikit-learn.org/stable/). You will learn how to:
+This tutorial guides you on how to use Feast with [Scikit-learn](https://scikit-learn.org/stable/). You will learn how to:
 
-1. Train a model locally \(on your laptop\) using data from [BigQuery](https://cloud.google.com/bigquery/)
-2. Test the model for online inference using [SQLite](https://www.sqlite.org/index.html) \(for fast iteration\)
-3. Test the model for online inference using [Firestore](https://firebase.google.com/products/firestore) \(for production use\)
+* Train a model locally \(on your laptop\) using data from [BigQuery](https://cloud.google.com/bigquery/)
+* Test the model for online inference using [SQLite](https://www.sqlite.org/index.html) \(for fast iteration\)
+* Test the model for online inference using [Firestore](https://firebase.google.com/products/firestore) \(for production use\)
 
 Try it and let us know what you think!
 
diff --git a/docs/tutorials/real-time-credit-scoring-on-aws.md b/docs/tutorials/real-time-credit-scoring-on-aws.md
@@ -0,0 +1,29 @@
+---
+description: >-
+  Credit scoring models are used to approve or reject loan applications. In this
+  tutorial we will build a real-time credit scoring system on AWS.
+---
+
+# Real-time credit scoring on AWS
+
+When individuals apply for loans from banks and other credit providers, the decision to approve a loan application is often made through a statistical model. This model uses information about a customer to determine the likelihood that they will repay or default on a loan, in a process called credit scoring.
+
+In this example, we will demonstrate how a real-time credit scoring system can be built using Feast and Scikit-Learn on AWS, using feature data from S3.
+
+This real-time system accepts a loan request from a customer and responds within 100ms with a decision on whether their loan has been approved or rejected. 
+
+## [Real-time Credit Scoring Example](https://github.com/feast-dev/real-time-credit-scoring-on-aws-tutorial)
+
+This end-to-end tutorial will take you through the following steps:
+
+* Deploying S3 with Parquet as your primary data source, containing both [loan features](https://github.com/feast-dev/real-time-credit-scoring-on-aws-tutorial/blob/22fc6c7272ef033e7ba0afc64ffaa6f6f8fc0277/data/loan_table_sample.csv) and [zip code features](https://github.com/feast-dev/real-time-credit-scoring-on-aws-tutorial/blob/22fc6c7272ef033e7ba0afc64ffaa6f6f8fc0277/data/zipcode_table_sample.csv)
+* Deploying Redshift as the interface Feast uses to build training datasets
+* Registering your features with Feast and configuring DynamoDB for online serving
+* Building a training dataset with Feast to train your credit scoring model
+* Loading feature values from S3 into DynamoDB
+* Making online predictions with your credit scoring model using features from DynamoDB
+
+| ![](../.gitbook/assets/github-mark-32px.png)[ View Source on Github](https://github.com/feast-dev/real-time-credit-scoring-on-aws-tutorial) |
+| :--- |
+
+
diff --git a/docs/tutorials/tutorials-overview.md b/docs/tutorials/tutorials-overview.md
@@ -1,10 +1,10 @@
 # Overview
 
-These feast tutorials showcase how to use Feast to simplify end to end model training / serving. 
+These Feast tutorials showcase how to use Feast to simplify end to end model training / serving. 
 
 {% page-ref page="fraud-detection.md" %}
 
 {% page-ref page="driver-ranking-with-feast.md" %}
 
-
+{% page-ref page="real-time-credit-scoring-on-aws.md" %}
 
